{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Adversarial-Robustness-Toolbox for scikit-learn ExtraTreesClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.datasets import load_iris\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from art.estimators.classification import SklearnClassifier\n",
    "from art.attacks.evasion import ZooAttack\n",
    "from art.utils import load_mnist\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Training scikit-learn ExtraTreesClassifier and attacking with ART Zeroth Order Optimization attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_adversarial_examples(x_train, y_train):\n",
    "    \n",
    "    # Create and fit ExtraTreesClassifier\n",
    "    model = ExtraTreesClassifier()\n",
    "    model.fit(X=x_train, y=y_train)\n",
    "\n",
    "    # Create ART classifier for scikit-learn ExtraTreesClassifier\n",
    "    art_classifier = SklearnClassifier(model=model)\n",
    "\n",
    "    # Create ART Zeroth Order Optimization attack\n",
    "    zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=20,\n",
    "                    binary_search_steps=10, initial_const=1e-3, abort_early=True, use_resize=False, \n",
    "                    use_importance=False, nb_parallel=1, batch_size=1, variable_h=0.2)\n",
    "\n",
    "    # Generate adversarial samples with ART Zeroth Order Optimization attack\n",
    "    x_train_adv = zoo.generate(x_train)\n",
    "\n",
    "    return x_train_adv, model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.1 Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(num_classes):\n",
    "    x_train, y_train = load_iris(return_X_y=True)\n",
    "    x_train = x_train[y_train < num_classes][:, [0, 1]]\n",
    "    y_train = y_train[y_train < num_classes]\n",
    "    x_train[:, 0][y_train == 0] *= 2\n",
    "    x_train[:, 1][y_train == 2] *= 2\n",
    "    x_train[:, 0][y_train == 0] -= 3\n",
    "    x_train[:, 1][y_train == 2] -= 2\n",
    "    \n",
    "    x_train[:, 0] = (x_train[:, 0] - 4) / (9 - 4)\n",
    "    x_train[:, 1] = (x_train[:, 1] - 1) / (6 - 1)\n",
    "    \n",
    "    return x_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_results(model, x_train, y_train, x_train_adv, num_classes):\n",
    "    \n",
    "    fig, axs = plt.subplots(1, num_classes, figsize=(num_classes * 5, 5))\n",
    "\n",
    "    colors = ['orange', 'blue', 'green']\n",
    "\n",
    "    for i_class in range(num_classes):\n",
    "\n",
    "        # Plot difference vectors\n",
    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
    "                axs[i_class].plot([x_1_0, x_2_0], [x_1_1, x_2_1], c='black', zorder=1)\n",
    "\n",
    "        # Plot benign samples\n",
    "        for i_class_2 in range(num_classes):\n",
    "            axs[i_class].scatter(x_train[y_train == i_class_2][:, 0], x_train[y_train == i_class_2][:, 1], s=20,\n",
    "                                 zorder=2, c=colors[i_class_2])\n",
    "        axs[i_class].set_aspect('equal', adjustable='box')\n",
    "\n",
    "        # Show predicted probability as contour plot\n",
    "        h = .01\n",
    "        x_min, x_max = 0, 1\n",
    "        y_min, y_max = 0, 1\n",
    "\n",
    "        xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
    "\n",
    "        Z_proba = model.predict_proba(np.c_[xx.ravel(), yy.ravel()])\n",
    "        Z_proba = Z_proba[:, i_class].reshape(xx.shape)\n",
    "        im = axs[i_class].contourf(xx, yy, Z_proba, levels=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
    "                                   vmin=0, vmax=1)\n",
    "        if i_class == num_classes - 1:\n",
    "            cax = fig.add_axes([0.95, 0.2, 0.025, 0.6])\n",
    "            plt.colorbar(im, ax=axs[i_class], cax=cax)\n",
    "\n",
    "        # Plot adversarial samples\n",
    "        for i in range(y_train[y_train == i_class].shape[0]):\n",
    "            x_1_0 = x_train[y_train == i_class][i, 0]\n",
    "            x_1_1 = x_train[y_train == i_class][i, 1]\n",
    "            x_2_0 = x_train_adv[y_train == i_class][i, 0]\n",
    "            x_2_1 = x_train_adv[y_train == i_class][i, 1]\n",
    "            if x_1_0 != x_2_0 or x_1_1 != x_2_1:\n",
    "                axs[i_class].scatter(x_2_0, x_2_1, zorder=2, c='red', marker='X')\n",
    "        axs[i_class].set_xlim((x_min, x_max))\n",
    "        axs[i_class].set_ylim((y_min, y_max))\n",
    "\n",
    "        axs[i_class].set_title('class ' + str(i_class))\n",
    "        axs[i_class].set_xlabel('feature 1')\n",
    "        axs[i_class].set_ylabel('feature 2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Example: Iris dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### legend\n",
    "- colored background: probability of class i\n",
    "- orange circles: class 1\n",
    "- blue circles: class 2\n",
    "- green circles: class 3\n",
    "- red crosses: adversarial samples for class i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZOO: 100%|██████████| 100/100 [01:22<00:00,  1.21it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_classes = 2\n",
    "x_train, y_train = get_data(num_classes=num_classes)\n",
    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZOO: 100%|██████████| 150/150 [02:12<00:00,  1.13it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_classes = 3\n",
    "x_train, y_train = get_data(num_classes=num_classes)\n",
    "x_train_adv, model = get_adversarial_examples(x_train, y_train)\n",
    "plot_results(model, x_train, y_train, x_train_adv, num_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 Example: MNIST"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Load and transform MNIST dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, y_train), (x_test, y_test), min_, max_ = load_mnist()\n",
    "\n",
    "n_samples_train = x_train.shape[0]\n",
    "n_features_train = x_train.shape[1] * x_train.shape[2] * x_train.shape[3]\n",
    "n_samples_test = x_test.shape[0]\n",
    "n_features_test = x_test.shape[1] * x_test.shape[2] * x_test.shape[3]\n",
    "\n",
    "x_train = x_train.reshape(n_samples_train, n_features_train)\n",
    "x_test = x_test.reshape(n_samples_test, n_features_test)\n",
    "\n",
    "y_train = np.argmax(y_train, axis=1)\n",
    "y_test = np.argmax(y_test, axis=1)\n",
    "\n",
    "n_samples_max = 200\n",
    "x_train = x_train[0:n_samples_max]\n",
    "y_train = y_train[0:n_samples_max]\n",
    "x_test = x_test[0:n_samples_max]\n",
    "y_test = y_test[0:n_samples_max]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Train ExtraTreesClassifier classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ExtraTreesClassifier(n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, \n",
    "                             min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', \n",
    "                             max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, \n",
    "                             bootstrap=False, oob_score=False, n_jobs=None, random_state=None, verbose=0, \n",
    "                             warm_start=False, class_weight=None)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ExtraTreesClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
       "                     criterion='gini', max_depth=None, max_features='auto',\n",
       "                     max_leaf_nodes=None, max_samples=None,\n",
       "                     min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                     min_samples_leaf=1, min_samples_split=2,\n",
       "                     min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "                     oob_score=False, random_state=None, verbose=0,\n",
       "                     warm_start=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X=x_train, y=y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.3 Create and apply Zeroth Order Optimization Attack with ART"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "art_classifier = SklearnClassifier(model=model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "zoo = ZooAttack(classifier=art_classifier, confidence=0.0, targeted=False, learning_rate=1e-1, max_iter=100,\n",
    "                binary_search_steps=20, initial_const=1e-3, abort_early=True, use_resize=False, \n",
    "                use_importance=False, nb_parallel=10, batch_size=1, variable_h=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZOO: 100%|██████████| 200/200 [09:02<00:00,  2.71s/it]\n"
     ]
    }
   ],
   "source": [
    "x_train_adv = zoo.generate(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ZOO: 100%|██████████| 200/200 [06:07<00:00,  1.84s/it]\n"
     ]
    }
   ],
   "source": [
    "x_test_adv = zoo.generate(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.4 Evaluate ExtraTreesClassifier on benign and adversarial samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign Training Score: 1.0000\n"
     ]
    }
   ],
   "source": [
    "score = model.score(x_train, y_train)\n",
    "print(\"Benign Training Score: %.4f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0nqMDyyR9QdKztp+uLVsraYXtpZJC0i5Jl7ekQwAtNZ6jAz+QNNrxxfJJ+AEcEZgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcnWvO9DUldmvSfrPEYvmStrXtgYmjv4a08n9dXJvUvP7Oz4iPjxaoa0h8Asrt7dGRE9lDdRBf43p5P46uTepvf2xOwAkRwgAyVUdAusrXn899NeYTu6vk3uT2thfpZ8JAKhe1SMBABUjBIDkCAEgOUIASI4QAJL7H4v8SYP7urYSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_train[0, :].reshape((28, 28)))\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign Training Predicted Label: 5\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_train[0:1, :])[0]\n",
    "print(\"Benign Training Predicted Label: %i\" % prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Training Score: 0.5300\n"
     ]
    }
   ],
   "source": [
    "score = model.score(x_train_adv, y_train)\n",
    "print(\"Adversarial Training Score: %.4f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAPAElEQVR4nO3df6zV9X3H8dergDAQLXcUis4KopmudsXuxh/DqIupZU0TNYt1pGmY64brpK0bS7RkiW6xC2vUzq0dCU4mJmprq06yuKohRm1WmciYYqnaKnXI3UW8UdQ6hMt7f9zDdmfv/Zx77znn+z3wfj4Scs75vs853zdf7n3x+Z7v53y/jggByOsDdTcAoF6EAJAcIQAkRwgAyRECQHKEAJBcLSFge4nt523/xPa1dfRQYnuH7Wdtb7W9uQv6WWd7t+1tw5b12H7E9ouN21ld1t/1tl9tbMOttj9dY38n2H7U9nbbz9n+SmN5V2zDQn+VbENXPU/A9iRJL0j6pKSdkp6StDQiflRpIwW2d0jqjYg9dfciSbbPk/S2pDsi4vTGsq9LGoiI1Y0gnRUR13RRf9dLejsibqyjp+Fsz5M0LyK22J4p6WlJl0j6PXXBNiz091lVsA3rGAmcKeknEfFSRLwn6duSLq6hj8NGRDwuaeB9iy+WtL5xf72GfmhqMUp/XSMi+iJiS+P+W5K2SzpeXbINC/1Voo4QOF7Sfw57vFMV/oXHKCQ9bPtp28vrbmYUcyOiTxr6IZI0p+Z+RrLC9jON3YXadleGsz1f0hmSNqkLt+H7+pMq2IZ1hIBHWNZtc5cXR8QnJP22pKsaw12MzxpJCyUtktQn6aZ625FsHy3pXklXR8Teuvt5vxH6q2Qb1hECOyWdMOzxr0jaVUMfo4qIXY3b3ZLu19AuTLfpb+xLHtqn3F1zP/9PRPRHxGBEHJR0q2rehranaOgX7M6IuK+xuGu24Uj9VbUN6wiBpySdYnuB7aMk/a6kDTX0MSLbMxofzsj2DEkXSdpWflUtNkha1ri/TNIDNfbyCw79cjVcqhq3oW1Luk3S9oi4eVipK7bhaP1VtQ0rPzogSY1DHX8jaZKkdRHxtcqbGIXtkzT0v78kTZZ0V9392b5b0gWSZkvql3SdpH+SdI+kj0h6RdJlEVHLh3Oj9HeBhoaxIWmHpCsP7X/X0N+5kp6Q9Kykg43FqzS03137Niz0t1QVbMNaQgBA92DGIJAcIQAkRwgAyRECQHKEAJBcrSHQxVNyJdFfq7q5v27uTaq2v7pHAl39DyH6a1U399fNvUkV9ld3CACoWUuThWwvkXSLhmb+/UNErC49/yhPjWma8b+P92ufpmjqhNffafTXmm7ur5t7k9rf33/rHb0X+0b68t7EQ2AiJwc5xj1xli+c0PoATNym2Ki9MTBiCLSyO8DJQYAjQCshcDicHARAE5NbeO2YTg7SONSxXJKmaXoLqwPQCa2MBMZ0cpCIWBsRvRHR280fxABZtRICXX1yEABjM+HdgYg4YHuFpIf0fycHea5tnQGoRCufCSgiHpT0YJt6AVADZgwCyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHItXZoc1Zp80vxi/cBLO4p1Ty7/c0/60OxxdjQ+z//Z/GJ9cPrBYv3EhbuL9el/7GL9v24+qljf0vudYn3P4DvF+lnfXVmsn/ynTxbrdWkpBGzvkPSWpEFJByKitx1NAahOO0YCvxURe9rwPgBqwGcCQHKthkBIetj207aXt6MhANVqdXdgcUTssj1H0iO2fxwRjw9/QiMclkvSNE1vcXUA2q2lkUBE7Grc7pZ0v6QzR3jO2ojojYjeKZrayuoAdMCEQ8D2DNszD92XdJGkbe1qDEA1WtkdmCvpftuH3ueuiPh+W7o6Qk067ZRiPaZOKdZ3nv/BYv3ds48t1nuOLR/nfuLj5ePkdfuXn88s1v/6m0uK9Z7VM4r1l9e/Xayv7v9ksX7cE1Gsd6sJh0BEvCTp423sBUANOEQIJEcIAMkRAkByhACQHCEAJEcIAMk5orpjm8e4J87yhZWtr2qDF3yiWO+54WfF+p0LHm5nO4ed/TFYrP/m168u1ie/09rP8sxXDxTrU/e8W6zH5u6dK7cpNmpvDIx4wgVGAkByhACQHCEAJEcIAMkRAkByhACQHCEAJMd1B9po6vO7ivXPzP6PijrpjJV9ZxfrL71dvm7B7Qu/V6y/ebB8nH/u3/5rsd5ph+fZAppjJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHKcT6BCA1ecU6zvXVK+LsD8y58p1v/51afH3dNwN+z59WL9qfPL8wAG33izWI9zymeo3/HlYlkLlpbnWTy0a2ux/qnjFpVXcATjfAIARkUIAMkRAkByhACQHCEAJEcIAMkRAkByzBPoIpNm/3KxPvj6QLH+8l3l4/zPnbeuWD/zr75UrM/5Vr3f58fEtTRPwPY627ttbxu2rMf2I7ZfbNzOamfDAKozlt2B2yUted+yayVtjIhTJG1sPAZwGGoaAhHxuKT3j0MvlrS+cX+9pEva3BeAikz0g8G5EdEnSY3bOe1rCUCVOn6iUdvLJS2XpGma3unVARiniY4E+m3Pk6TG7e7RnhgRayOiNyJ6p2jqBFcHoFMmGgIbJC1r3F8m6YH2tAOgak13B2zfLekCSbNt75R0naTVku6x/QVJr0i6rJNNZjG45/WWXr9/71Etvf6jn/tRsf7amknlNzg42NL6UY+mIRARS0cpMesHOAIwbRhIjhAAkiMEgOQIASA5QgBIjhAAkuv4tGFU57RrXijWr/hY+ajuP564sVg//7KrivWZ33myWEd3YiQAJEcIAMkRAkByhACQHCEAJEcIAMkRAkByzBM4ggy+8Wax/voXTyvWX9nwbrF+7Q13FOtf/eylxXr8+7HF+glf+2GxrgqvkZEJIwEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJJzVHjs9Rj3xFnmTOXdauD3zynW77zuxmJ9weRpLa3/o3esKL//V5vMI8CoNsVG7Y0Bj1RjJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHLME8CYxeJFxfoxq3cW63ef9FBL6z/10T8o1n/1L8rnUxh88aWW1n84a2megO11tnfb3jZs2fW2X7W9tfHn0+1sGEB1xrI7cLukJSMs/0ZELGr8ebC9bQGoStMQiIjHJQ1U0AuAGrTyweAK2880dhdmta0jAJWaaAiskbRQ0iJJfZJuGu2Jtpfb3mx7837tm+DqAHTKhEIgIvojYjAiDkq6VdKZheeujYjeiOidoqkT7RNAh0woBGzPG/bwUknbRnsugO7WdJ6A7bslXSBptqR+Sdc1Hi+SFJJ2SLoyIvqarYx5Ake2SXPnFOu7Lj+5WN90zS3F+gea/J/1uZcvKtbfPPf1Yv1IVpon0PTiIxGxdITFt7XcFYCuwLRhIDlCAEiOEACSIwSA5AgBIDlCAEiO8wmga9yzs3xdgek+qlj/ebxXrH/mS1eX3//+TcX64YzrDgAYFSEAJEcIAMkRAkByhACQHCEAJEcIAMk1/SoxcMjBc8vXHfjpZdOK9dMX7SjWm80DaObvBs4ov/8Dm1t6/yMVIwEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJJjnkAi7j29WH/hy+Xj9LcuXl+snzet/H3+Vu2L/cX6kwMLym9wsOmlMVJiJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHLMEziMTF5wYrH+0yuOK9avv/zbxfrvHL1n3D2106r+3mL9sVvOLtZnrS9ftwAjazoSsH2C7Udtb7f9nO2vNJb32H7E9ouN21mdbxdAu41ld+CApJURcZqksyVdZfvXJF0raWNEnCJpY+MxgMNM0xCIiL6I2NK4/5ak7ZKOl3SxpEPzSNdLuqRTTQLonHF9MGh7vqQzJG2SNDci+qShoJA0p93NAei8MYeA7aMl3Svp6ojYO47XLbe92fbm/do3kR4BdNCYQsD2FA0FwJ0RcV9jcb/teY36PEm7R3ptRKyNiN6I6J2iqe3oGUAbjeXogCXdJml7RNw8rLRB0rLG/WWSHmh/ewA6bSzzBBZL+rykZ21vbSxbJWm1pHtsf0HSK5Iu60yLR47J8z9SrL/5G/OK9cv/8vvF+h998L5ivdNW9pWP4//w78vzAHpu/7difdZB5gF0QtMQiIgfSPIo5Qvb2w6AqjFtGEiOEACSIwSA5AgBIDlCAEiOEACS43wC4zB53oeL9YF1M4r1Ly54rFhfOrN/3D2104pXzy3Wt6xZVKzP/t62Yr3nLY7zdyNGAkByhACQHCEAJEcIAMkRAkByhACQHCEAJJdqnsB7nyp/n/29Pxko1led/GCxftEvvTPuntqpf/DdYv28DSuL9VP//MfFes8b5eP8B4tVdCtGAkByhACQHCEAJEcIAMkRAkByhACQHCEAJJdqnsCOS8qZ98LHvtvR9X/rjYXF+i2PXVSse3C0M78POfWGl4v1U/o3FeuDxSqOVIwEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIzhFRfoJ9gqQ7JH1YQ18ZXxsRt9i+XtIfSnqt8dRVEVH8wv0x7omzzNXMgaptio3aGwMjTjQZy2ShA5JWRsQW2zMlPW37kUbtGxFxY7saBVC9piEQEX2S+hr337K9XdLxnW4MQDXG9ZmA7fmSzpB0aP7pCtvP2F5ne1abewNQgTGHgO2jJd0r6eqI2CtpjaSFkhZpaKRw0yivW257s+3N+7WvDS0DaKcxhYDtKRoKgDsj4j5Jioj+iBiMiIOSbpV05kivjYi1EdEbEb1TNLVdfQNok6YhYNuSbpO0PSJuHrZ83rCnXSqpfElaAF1pLEcHFkv6vKRnbW9tLFslaantRZJC0g5JV3akQwAdNZajAz+QNNLxxfJJ+AEcFpgxCCRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAcoQAkBwhACRHCADJEQJAck2vO9DWldmvSfrZsEWzJe2prIHxo7/WdHN/3dyb1P7+ToyID41UqDQEfmHl9uaI6K2tgSborzXd3F839yZV2x+7A0ByhACQXN0hsLbm9TdDf63p5v66uTepwv5q/UwAQP3qHgkAqBkhACRHCADJEQJAcoQAkNz/AAXOU/KcAqcYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_train_adv[0, :].reshape((28, 28)))\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Training Predicted Label: 3\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_train_adv[0:1, :])[0]\n",
    "print(\"Adversarial Training Predicted Label: %i\" % prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign Test Score: 0.6100\n"
     ]
    }
   ],
   "source": [
    "score = model.score(x_test, y_test)\n",
    "print(\"Benign Test Score: %.4f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_test[0, :].reshape((28, 28)))\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Benign Test Predicted Label: 7\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_test[0:1, :])[0]\n",
    "print(\"Benign Test Predicted Label: %i\" % prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Test Score: 0.3550\n"
     ]
    }
   ],
   "source": [
    "score = model.score(x_test_adv, y_test)\n",
    "print(\"Adversarial Test Score: %.4f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ub3+cDgDJEQJAck2HwOqG998K/bWnl/vr5d6kGvtr9JoAgOY1PRIA0DBCAEiOEACSIwSA5AgBILn/BV1w3NO3uf28AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.matshow(x_test_adv[0, :].reshape((28, 28)))\n",
    "plt.clim(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial Test Predicted Label: 4\n"
     ]
    }
   ],
   "source": [
    "prediction = model.predict(x_test_adv[0:1, :])[0]\n",
    "print(\"Adversarial Test Predicted Label: %i\" % prediction)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
